How to Build a Multi-Agent AI Research Assistant Using OpenAI SDK and Web Scraping
multi-agent AI
OpenAI Agents SDK
web scraping
GPT-5.4
AI research assistant
Python programming
structured reports
5
Excellent Deep-Dive Tutorial (Builder Grade)
Media Hype
4/10
Real Impact
5/10
What is the Viqus Verdict?
We evaluate each news story based on its real impact versus its media hype to offer a clear and objective perspective.
AI Analysis:
Moderate impact stems from its high practical value in showing advanced integration techniques, but the concept itself (agents + RAG) is not paradigm-shifting and is a common pattern currently circulating among AI developers.
Article Summary
This article is a technical tutorial providing a comprehensive walkthrough for building a sophisticated multi-agent AI application. It uses the OpenAI Agents SDK, a miniature LLM (GPT-5.4 mini), and the Olostep Web API to create a research assistant. The system is designed to orchestrate specialized agents (manager, judge, analyst) that perform web searches, scrape rich Markdown content, and progressively gather, evaluate, and structure evidence from online sources. The final output is a structured, source-grounded research report, which can be deployed as an interactive web application.Key Points
- The architecture utilizes a manager agent to coordinate specialized sub-agents and external tools, enabling complex tasks like web searching and data scraping.
- Integration of the Olostep Web API allows the agents to move beyond simple search snippets, scraping rich Markdown content directly from search results.
- The entire workflow is executable in Python, with helper functions provided to manage APIs, data serialization, and environment setup, culminating in a deployable web app.